1. Field of the Invention
The present invention relates generally to clinical diagnostic processes, and more particularly to systems and methods for implementing quality control in clinical diagnostic processes.
2. Description of Related Art
Clinical diagnostic laboratories employ various schemes to control the clinical diagnostic process to ensure the accuracy of diagnostic results. In the United States, Westgard is a well-known scheme, with other schemes, such as RiliBAK, more common outside of the U.S. More recently developed patient-data based schemes, such as a Biometric Quality Control process as described in U.S. Pat. No. 7,203,619 are also becoming more widely used.
Regardless of the specific quality control (QC) process employed, a common characteristic of known QC processes is the requirement for operator intervention to initiate and/or conduct the quality control process. However, operator intervention to conduct the quality control process does not necessarily occur as necessary or when required due to a variety of reasons. For example, many labs may not understand how to apply the QC rules such that frequent error flags lead to indifference on the part of the test operator who may simply ignore the ostensible error and choose not to conduct a quality control process. Thus, a too high QC false rejection rate may lead to an operator ignoring a signal or indication that a quality control process run should be undertaken. A College of American Pathologists (CAP) Q-Probe study conducted in 1994 found that many laboratories respond to a QC error flag by merely repeating the control. No reasoned troubleshooting occurs unless the test operator is unsuccessful in getting the control value to fall within acceptable limits. Reasons identified in the study for not immediately troubleshooting included the perception that it is easier to re-test than troubleshoot, laziness, lack of knowledge, habit, and no accountability to troubleshoot correctly.
As addressed in the Biometric Quality Control process invention of U.S. Pat. No. 7,203,619, rather than accept that some type of error might be present in the test system when a statistical flag occurs, labs may move immediately to some form of remedy rather than troubleshooting. The basic premise is that the statistical control system they use creates too many unwarranted errors so they automatically assume the error flag is false. The quickest remedy in this environment is to get the control value within range. To do so, some labs may repeat the control in hopes that the next value will be within limits, repeat with fresh control product, check or repeat calibration, or make up fresh reagent. Sometimes limited troubleshooting may be employed, including, for example, testing of assayed control materials to detect systematic error, looking at a history of control outliers, and calling the manufacturer for guidance or word of any national performance trends. Each of these actions is taken without any reasonable justification other than one of them usually corrects the error at least temporarily. Typically, the most common causes of QC error flags include random error, environmental conditions, control range too tight or incorrectly calculated, reagent (lot change, deterioration, contamination), control problems, calibration, sampling error, instrument malfunction, and poor maintenance.
Laboratory staff typically consider troubleshooting to be complex and often unguided. The production atmosphere of a typical lab and limited resources may contribute to a philosophy of avoiding troubleshooting unless absolutely necessary. The assumption follows that if troubleshooting could be focused, guided, or deemed necessary and productive, laboratory staff would engage in the effort. In general, it is desirable to make troubleshooting far easier by, for example, providing a QC system that identifies actionable error (i.e., eliminates false error detection), providing online troubleshooting advice, providing interactive online user groups so labs can exchange information readily, basing analytical process control on medical relevance limits (where appropriate), providing an analysis of the most frequently observed errors and determining the most likely cause of the error flag, providing instrument-specific troubleshooting guides, posting control stability claims and interlabs online, providing method group statistics, providing continuing education, and providing parallel lots for troubleshooting.
Thus, it is apparent that current quality control processes relying on operator intervention suffer from numerous drawbacks, often leading to misapplication of the quality control process itself.